Expert annotation of 3D medical image for downstream analysis is resource-intensive,
posing challenges in clinical applications. Visual self-supervised learning (vSSL), though …

In this paper, we propose a new RGB-thermal image segmentation method based on
parameter sharing and attention fusion for safe autonomous driving. An encoder-decoder …

Glioma segmentation is a crucial task in computer-aided diagnosis, requiring precise
discrimination between lesions and normal tissue at the pixel level. Popular methods …

Accurate and fast segmentation of 3D medical images is crucial in clinical analysis. CNNs
struggle to capture long-range dependencies because of their inductive biases, whereas the …

Standard modern machine-learning-based imaging methods have faced challenges in
medical applications due to the high cost of dataset construction and, thereby, the limited …

Complicated image registration is a key issue in medical image analysis, and deep learning-
based methods have achieved better results than traditional methods. The methods include …

Standard modern machine-learning-based imaging methods have faced challenges in
medical applications due to the high cost of dataset construction and, thereby, the limited …

In this study, we introduce a multi-modal approach that efficiently integrates multi-scale
clinical and dermoscopy features within a single network, thereby substantially reducing …

High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed
anatomical structural information, enabling precise segmentation of regions of interest for …

In this dissertation, we fill this gap by investigating the use of multi-modal deep learning
methods in skin lesion classification. We investigate various multi-modal scenarios for skin …